A visualizer for the core search algorithms used in AI and game development. Special thanks to Amit Patel and his cool tutorials for providing me with the inspiration. #MadeWithUnity

This repository contains implementation of different AI algorithms, based on the 4th edition of amazing AI Book, Artificial Intelligence A Modern Approach

CSE 571 Artificial Intelligence

GraphLab is an application that shows visually how several graph algorithms work

Find the shortest route using A* algorithm and graphs (Route Planner application)

Visualization for multiple searching algorithms.

A fine-tuned visual implementation of Informed and Uninformed Search Algorithms such as Breadth First Search, Depth First Search, Uniform Cost Search, A* Search, Greedy First Search

Implement Algorithms For Graph Search (like A*) & Local Search (like hill climbing algorithms) & Genetics

Desktop app for visualizing graph search algorithms

DFS / BFS / IDS / UCS / Bi-directional / A* / IDA* / SMA* / ...

Uniform Cost Search implementation for Artificial Intelligence course

Programs developed for CSCI561 Foundations of Artificial Intelligence course

All Artificial Intelligence Search algorithms. Depth first search, Breadth first search, uniform cost search, Greedy search, A star search, Minimax and Alpha beta pruning. Implemented in Python 3.

Planning Project Implementation for the Udacity Artificial Intelligence Nanodegree Program

classical search algorithms are used in AI | AI course - Fall 2018

java, discrete map search, uniform cost search algorithm

The algorithm determines the least cost path from the start location to goal location

Calculating the shortest path between two nodes with the Uniform Cost Search algorithm.

Program that searches for the shortest route using the 'Uniform Cost Search' algorithm by consulting a map of the province of Santo Domingo extracted from OpenStreetMap.

Attempts to solve the 8-Puzzle by using various heuristic search methods. Written in Python.